Deep learning-based classification of the mouse estrous cycle stages

Kyohei Sano,Chihiro Sutoh,Eiji Shimizu,Suguru Tohyama,Shingo Matsuda,Daisuke Komura

doi:10.1038/s41598-020-68611-0

Abstract

There is a rapidly growing demand for female animals in preclinical animal, and thus it is necessary to determine animals' estrous cycle stages from vaginal smear cytology. However, the determination of estrous stages requires extensive training, takes a long time, and is costly; moreover, the results obtained by human examiners may not be consistent. Here, we report a machine learning model trained with 2,096 microscopic images that we named the "Stage Estimator of estrous Cycle of RodEnt using an Image-recognition Technique (SECREIT)." With the test dataset (736 images), SECREIT achieved area under the receiver-operating-characteristic curve of 0.962 or more for each estrous stage. A test using 100 images showed that SECREIT provided correct classification that was similar to that provided by two human examiners (SECREIT: 91%, Human 1: 91%, Human 2: 79%) in 11 s. The SECREIT can be a first step toward accelerating the research using female rodents.

Highlights

There is a rapidly growing demand for female animals in preclinical animal, and it is necessary to determine animals’ estrous cycle stages from vaginal smear cytology
Determining the estrous stage of a rodent by using vaginal cytology evaluated by a human examiner has some problems: (1) a long training period is required in order to become skillful; (2) it takes a long time to determine the estrous stage from images, and doing so can be costly; and (3) the evaluations sometimes do not fully match among human examiners
We developed an automatic estrous cycle stage classifier with a deep learning algorithm, and the results of our analyses demonstrated that the model achieved high sensitivity, specificity, and area under ROC curve (AUC)

Summary

Introduction

There is a rapidly growing demand for female animals in preclinical animal, and it is necessary to determine animals’ estrous cycle stages from vaginal smear cytology. In many studies of rodents, the estrous cycle stage of each animal has been determined by vaginal cytology. Machine learning algorithms powered by computational advances and large-scale datasets have provided dramatic progress, especially in visual tasks such as object recognition and visual classification Such algorithms have been applied to medical fields, and they have performed comparably or better than humans in some fields including the diagnosis of skin r ashes[20], and the evaluations of chest X-rays[21] and histopathological images[22,23]. We developed a classifier of estrous stage using machine learning and named it the "Stage Estimator of estrous Cycle graduate.chiba‐u.jp

Methods

Results

Conclusion